April 16, 2021


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“TL;DR:” Out-of-Context Adversarial Text Summarization and Hashtag Recommendation. (arXiv:2104.00782v1 [cs.CL])

This paper presents Out-of-Context Summarizer, a tool that takes arbitrary
public news articles out of context by summarizing them to coherently fit
either a liberal- or conservative-leaning agenda. The Out-of-Context Summarizer
also suggests hashtag keywords to bolster the polarization of the summary, in
case one is inclined to take it to Twitter, Parler or other platforms for
trolling. Out-of-Context Summarizer achieved 79% precision and 99% recall when
summarizing COVID-19 articles, 93% precision and 93% recall when summarizing
politically-centered articles, and 87% precision and 88% recall when taking
liberally-biased articles out of context. Summarizing valid sources instead of
synthesizing fake text, the Out-of-Context Summarizer could fairly pass the
“adversarial disclosure” test, but we didn’t take this easy route in our paper.
Instead, we used the Out-of-Context Summarizer to push the debate of potential
misuse of automated text generation beyond the boilerplate text of responsible
disclosure of adversarial language models.